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12 results for “Concrete structures”
VoroCrack3d: An annotated data set of 3d CT concrete images with synthetic crack structures
<p>VoroCrack3d is an annotated data set of 3d CT images of concrete with synthetic crack structures. Its main purpose is the training and testing of machine learning models for 3d crack segmentation. The data set comprises 1344 images together with their corresponding ground truths. The concrete backgrounds are cropped out sections of size 400x400x400 voxels of CT images of concrete. To this end, several different concrete samples were scanned (normal concrete (NC), high-performance concrete (HPC), ultra-high-performance concrete (UHPC), air pore concrete; without and with reinforcements (straight steel fibers, crimped steel fibers, hooked-end steel fibers, polypropylene fibers, fibers made of glass fiber-reinforced polymer). The original concrete images have a resolution between 2.8 and 106 micrometers.</p> <p>The crack structures are modeled via minimum-weight surfaces in Voronoi diagrams according to the paper</p> <p>[1] C. Jung, C. Redenbach, Crack Modeling via Minimum-Weight Surfaces in 3d Voronoi Diagrams, Journal of Mathematics in Industry, 13, 10 (2023). https://doi.org/10.1186/s13362-023-00138-1.</p> <p>The surfaces are discretized, dilated and superimposed on the concrete backgrounds.</p> <p>The data set offers a high variety regarding concrete types, noise levels and crack widths, shapes, regularity and branching. This makes it suitable for studying the generalizability and robustness of 3d crack segmentation methods.</p> <p>______________________________________________________________________________________________</p> <p>The folder 'data' contains seven subfolders, each containing the data generated from a specific concrete type (NC, HPC, air pore concrete, polypropylene fiber-reinforced concrete, steel fiber-reinforced concrete (straight, crimped and hooked-end steel fibers)).</p> <p>Each subfolder again contains four subfolders according to the point process model that was used for generating the 3d Voronoi diagrams. The point processes and Voronoi diagrams are restricted to windows of size 400x150x400. </p> <p>- 'hc': Hard core point process with 60% volume density and intensity 0.000025 obtained from force-biased sphere packing.<br>- 'matclust': Matérn cluster process with parent intensity 0.0002/50, offspring intensity 50 and cluster radius 20.<br>- 'ppp': Poisson point process with intensity 0.0002.<br>- 'ppp-scaled': Poisson point process with intensity 0.0002 (but inside 200x150x200 window). The resulting Voronoi diagram is stretched in x- and z- direction by a factor of 2.</p> <p>Each of these contains five subfolders: one for the 3d input images, two for the corresponding labels (ground truths; one with and one without pores/fibers), one for the input and label previews (slice z=200 for each of the images) and a misc folder containing the concrete background without crack and, if applicable, the pore/fiber segmentation image.</p> <p>The data itself then contains 48 images:<br>1a-1d: crack with up to seven branches; fixed crack width (~1 voxel).<br>2a-2d: crack with up to four branches; fixed crack width (~1 voxel).<br>3a-3d: crack with up to one branch; fixed crack width (~1 voxel).<br>4a-4d: crack with no branches; fixed crack width (~1 voxel).<br>5a-5d: crack with no branches; fixed crack width (~3 voxels).<br>6a-6d: crack with no branches; fixed crack width (~5 voxels).<br>7a-7d: crack with no branches; fixed crack width (~7 voxels).<br>8a-8d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.01);<br>9a-9d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.02);<br>10a-10d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.05);<br>11a-11d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.1);<br>12a-12d: crack with up to seven branches; multiscale crack (bernoulli parameter 0.2);</p> <p>The names 'a'-'d' indicate level of added noise added to the image:<br>a: None.<br>b: Uniformly on [-sigma,sigma] <br>c: Uniformly on [-2*sigma,2*sigma] <br>d: Uniformly on [-4*sigma,4*sigma] <br>Negative values are mapped to 0. <br>For inputs of type int, noise values are rounded to the nearest integer.<br>(sigma = standard deviation of voxel greyvalues in image)</p> <p>Note that the grey values in the ground truths correspond to the local crack width. They can be thresholded to obtain binary masks.</p> <p>For more details, we refer to [1].</p>
Data to publication: Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
<p>This dataset contains the results of an experimental campaign, presented in the publication "Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures". The publication covers fibre optic measurements inside large-scale specimens subjected to external load. The specimens comprised reinforced concrete slabs, strengthened with reinforcement bars made from iron-based shape memory alloy.</p>
Dataset: Long-Term Resistance of Gradient Anchorage for Prestressed CFRP Strips in Structural Concrete Retrofitting
<p>This dataset presents experimental research results on the long-term behaviour of a non-mechanical prestressed CFRP anchorage for concrete retrofitting.<br> The following data is included:<br> - Force-slip of lap-shear tests.<br> - Slip profiles (at last stage) of prestress force releasing and lap-shear tests.</p> <p> </p> <p>Further details on the experimental setup, as well as representation of datasets can be found in the following works.<br> Please cite the following works, if any part of the dataset is used within your research.</p> <p>https://doi.org/10.1016/j.compositesb.2017.11.062<br> https://doi.org/10.3390/polym10060565<br> Harmanci, Yunus Emre. Long-Term Resistance of Gradient Anchorage for Prestressed CFRP Strips in Structural Concrete Retrofitting. Diss. ETH Zurich, 2018.</p>
Dataset for publication Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7
<p>Open dataset for publication Sikora P., El-Khayatt A.M., Saudi H.A., Liard M., Lootens D., Chung S.-Y., Woliński P., Abd Elrahman M. Rheological, mechanical, microstructural and radiation shielding properties of cement pastes containing magnetite (Fe3O4) nanoparticles. International Journal of Concrete Structures and Materials (2023), 17, 7. https://doi.org/10.1186/s40069-022-00568-y</p> <p>File 1 - X-ray diffractogram and particle size distribution (laser granulometry) data - *.opju (Origin)<br> File 2 - Rheological test results - *.opju (Origin)<br> File 5 - Mechanical peformance (early strength - ultrasounds and compressive strength) and density test results - *.opju (Origin)<br> File 4 - Mercury intrusion porosimetry test data - *.opju (Origin)</p>
NCCD-PF - A pre-failure narrow concrete cracks dataset for engineering structures damage classification and semantic segmentation
<p>The NCCD-PF dataset was developed for the classification and semantic segmentation of narrow concrete cracks in engineering structures elements at the pre-failure state. It only includes cracks whose width is narrower than 0.3 mm, i.e. the limit value specified in EC 1992-1-1 for typical elements of engineering structures and environmental conditions.</p> <p>This dataset is dedicated to the early crack detection at a stage when the serviceability limit state has not yet been exceeded and the failure of a structural element has not occurred. By implementing the early crack detection approach, it is possible to protect cracks in order to stop or slow down their propagation and thus to extend the structure's lifespan.</p> <p>This dataset contains images of cracks appearing on various elements of engineering structures (bridges, viaducts, tunnels) made of reinforced concrete (including abutments, tunnel walls, concrete barriers, pillars). The images were captured on construction sites and during inspections of engineering structures, at different stages of the reinforced concrete structure's working conditions - from the construction stage (when the elements are loaded only by their own weight) to the structure's use stage (when the elements are loaded by most of the design loads). The images are also differentiated by the cause of the cracking (ex., thermal and shrinkage stresses in young concrete, excessive stresses). The images were acquired using fixed-focus cameras without prior conditioning in order to represent the real working conditions of a bridge engineer during structural inspections. The images are characterised by a high degree of complexity due to the quality of the concrete surface finish (e.g. presence of formwork marks, concrete trowel marks), which could potentially be recognised as cracks.</p> <p>This dataset is dedicated to researchers working in the fields of computer vision, machine learning and deep learning. In particular, it contains domain knowledge in structural health monitoring, so that it can support the work of engineers in detecting cracks of concrete elements in a pre-failure state.</p> <p>A detailed description of the dataset is presented in <a href="https://www.nature.com/articles/s41597-023-02839-z" target="_blank" rel="noopener">A pre-failure narrow concrete cracks dataset for engineering structures damage classification and segmentation</a> (DOI: 10.1038/s41597-023-02839-z).</p>
Electromagnetic Wave Dataset for Strength Degradation Detection in Reinforced Concrete Structures Using RFID Measurements and CNN Model
<p>This dataset comprises 1,800 electromagnetic wave (EM-wave) images collected from three different reinforced concrete beams subjected to varying levels of corrosion. Each image is classified into 'normal' or 'reduced strength' categories based on the beam's structural integrity. Generated through a non-destructive RFID-based monitoring technique, this dataset integrates advanced analyses like 2-D Fourier transforms and fractal dimensions. It is specifically designed to train and validate Convolutional Neural Networks (CNNs) for detecting strength degradation in reinforced concrete structures.</p>
A Comprehensive Reliability-Based Framework for Corrosion Damage Monitoring and Repair Design of Reinforced Concrete Structures
<p>Corresponding data set for Tran-SET Project No. 17STLSU03. Abstract of the final report is stated below for reference:</p> <p>"In this work, we developed a comprehensive framework for corrosion management of reinforced concrete (RC) structures. This framework includes critical steps of an effective approach to quantify the damage evolution as well as providing the timeframe for effective maintenance/repair strategies for corrosion assessment in RC structures. The framework included several activities including the use of indirect and direct inspection tools, theoretical development for damage prediction, experimental measurements and theoretical development of repair time based on reliability. The uniqueness of the framework is the integration of deterministic modeling of corrosion damage evolution by using mechanistic analysis with statistical modeling on corrosion of RC structures by using measurements from the field (or natural environment) and experimental testing (in the laboratory). The framework includes a simple algorithm that relies in each development and task generated from this work to monitor and estimate the status of the reinforced concrete structures and the most suitable strategy to extend the life of the system while maximizing the reliability."</p>
Reduction of Structural Damage from the Thermal Expansion of Concrete Using Multifunctional Materials
<p>Corresponding data set for Tran-SET Project No. 18STTAM01. Abstract of the final report is stated below for reference:</p> <p>"This study leveraged past successes in the analysis and design of shape memory alloy (SMA) components to address the issue of thermal expansion in concrete structures. Since the SMA used in the current work is relatively cheaper than other common SMAs (less than $50/lb compared to NiTi which is $200/lb due to difficulties in processing), it is anticipated that the findings of the study could be implemented in real infrastructures made of concrete, asphalt concrete, and other complex large infrastructure. Low-cost Fe-SMAs and other multifunctional materials can be considered as a replacement for components made of steel (e.g., in reinforced or plain jointed concrete pavements) to control distresses resulting from thermal expansion during seasonal/daily temperature change. This study conducted a series of finite element (FE) case studies of various configurations of concrete (blocks, slabs, and beams) with embedded, pre-strained SMA rods. This included developing new models to investigate temperature induced deflection in concrete slabs to analyze their curling behavior. It also included investigating the optimal position of the SMA rod and required rod radius. It is hoped that the results of this work could help to design smarter civil infrastructure incorporating multifunctional materials into established civil engineering materials."</p>
Code from: Structural design to mitigate concrete GHG emissions
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Supplementary information to "Material intensity and embodied CO2 benchmark for reinforced concrete structures in Brazil"
<p>This Excel file contains the electronic supplementary information to the manuscript " Material intensity and embodied CO<sub>2</sub> benchmark for reinforced concrete structures in Brazil", including detailed structural design data for the 53 analyzed buildings and the calculation of the structural material quantity and embodied CO2 indicators.</p>
A holistic life cycle design approach to enhance the sustainability of concrete structures
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Ductility and brittleness of lightweight aggregate concrete structures
<p>Here are all database that is part of my PhD thesis and it is called in thesis Annex B.</p>
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